every job will turn into explaining your intentions to ai explaining what you want to ai is surpringly time consuming, coders already spend 80% of their time doing it, and this will be true for everyone

Token costs are why there will be no saas apocalypse / good dev tools are cached intelligence for agents! The popular theory goes: agents can write code, so they'll just rebuild every tool from scratch and hit raw APIs. no more dev tools, no more CLIs, no more software layers. just agents and endpoints! We just tested this and the data says the opposite. We benchmarked Claude Code and Codex on real Hugging Face Hub tasks (~1,000 graded runs), with two setups: the agent-optimized hf CLI vs the agent hand-rolling curl or SDK calls from scratch. Hand-rolling burns up to 6x more tokens on multi-step tasks and fails more often (84% vs 94% task success). And that's just dropping one abstraction layer. It would obviously be orders of magnitude more tokens and a dramatically higher failure rate if the agent tried to bypass HF altogether and rebuild model hosting, versioning, and distribution from scratch. Every time an agent re-derives a workflow from raw API calls, you pay for that reasoning in tokens. every single run. a good CLI compresses that entire chain into a few high-level commands the agent can't get wrong. In a world where everyone is complaining tokens are too expensive, abstraction is leverage: thousands of hours of design decisions your agent doesn't have to re-reason about at inference time. Good tools are cached intelligence for agents! So no, agents won't rebuild everything from scratch. they'll gravitate to the most token-efficient tools, because that's what their owners pay for. The software that survives won't just be accessible to agents, it will be accurate and cheap for them to drive. We're seeing it happen with HF, which is becoming the platform for agents to use AI: ~49M requests in just two months, and growing fast! huggingface.co/blog/hf-cli-f…

My base case is model capability improvements will far outpace enterprise ability to adapt and instead of patiently waiting the labs will verticalize, gatekeep and kingmake with revenue share agreements for frontier model access. A recurring theme in all my posts across geopol and tech is the default assumption that most people are retarded, game theory is under utilized, and you have to be Machiavellian if you want to get shit done. In this case you need to put the fear of god into the board members of every public company. If you work at a lab and don’t understand how to go about doing this my DMs are open.

[7/7] Perhaps understanding can be harder than solving. Nesterov’s AGD was discovered decades ago, yet interpretations came much later. @weijie444 Or perhaps understanding is something only humans need. Machines do not understand mathematics; they are just freakishly good at it.

Images as code Images are represented as code, so every part of an image becomes addressable, editable, and manipulatable.

AI’s Shadow Output Gap While Washington obsesses over debt and inflation, AI is already ushering in an age of abundance (Part 1) ⸻ The political and economic establishment can’t stop talking about deficits, debt, and the CPI. Capitol Hill hearings, FOMC minutes, and financial news all pulse to the same beat. Yet this fixation ironically coincides with the arrival of the most powerful productivity engine in human history: generative AI. Its impact is creating a shadow output gap — an invisible but rapidly widening expansion of supply-side capacity. Policymakers, especially at the Federal Reserve, act as if the boom doesn’t exist. The real risk is not inflation. It is a stealth supply shock that pushes prices, wages, and term premia down. Deficits may prove too small. Monetary policy may already be too tight. ⸻ Productivity Everywhere — Except in the Data This is Solow’s Paradox, redux: “We see the computer age everywhere except in the productivity statistics.” Only this time the curve is ten-times steeper. Previous tech waves required hardware diffusion—mainframes, PCs, smartphones. AI requires none of that; it arrives through an app. That frictionless uptake already generates latent productivity that never reaches GDP because it appears as: •lower input costs (fewer billable hours), •consumer surplus (time saved, spending skipped), and •silent substitution (high-skill labor quietly displaced). Illustrations abound: •A patient triages symptoms with ChatGPT and skips four clinic visits. •An analyst masters a new industry without three costly expert calls. •A five-person start-up closes a seed round with no CFO, lawyer, or recruiter—AI fills those roles off the books. Each case creates real value, but none is logged as “output.” ⸻ Counting the Invisible Token Economy Tokens — the fragments of text an AI model processes — are the kilowatt-hours of knowledge work. Track them and you watch the shadow gap in real time. •Google’s token throughput grew 50-fold year-over-year as usage soared and per-token cost collapsed. •OpenAI’s models now sit in support desks, research departments, and legal teams worldwide. •Rapidly falling costs are unlocking accelerating demand across every provider. The data-center capex from Nvidia, Microsoft, and other hyperscalers is simply the physical expression of this surge. (1/2). $NVDA $AMZN $GOOGL $MSFT $TSM $CRWV $NBIS